1. Field of the Disclosure
The present disclosure relates to a multidimensional correlated data extracting device and a multidimensional correlated data extracting method.
2. Discussion of the Background Art
Multidimensional data is data configured by values of various elements. As a specific example thereof, there is “purchase data” having product names and quantities of customer's purchased products at one-time purchase, a shopping time, and the like as elements such as POS (registered trademark) data of a supermarket. Hereinafter, the data of such sources will be referred to as “original data”.
In finding out a correlation of data, it is relatively easy to find out a positive correlation between two elements (two dimensions). For example, a service has been provided which searches for documents having close contents from among a large amount of documents (for example, see Patent Literature 1). Speaking of purchase data, there may be a positive correlation between beef steak meat and a potato.
However, there are many cases where a correlation between elements that cannot be assumed cannot be found. The reason for this is that, as long as a technique of repeatedly setting a hypothesis and verifying the hypothesis is used, generally, it is difficult to set hypotheses and verify the hypotheses covering all the possibilities due to temporal restrictions and the like. In other words, it can be stated that since it is difficult to set an “unexpected” hypothesis, it is difficult to acquire an “unexpected” finding. In terms of purchase data, even when there is a correlation between beef meat and a detergent, it is considered to be difficult to find out such a correlation. In addition, for a correlation among three (three dimensions) or more elements, there are a large number of combinations thereof, and the difficulty rapidly increases.
A purchase pattern in a supermarket will be described as an example. Even when there are relatively many customers of a specific combination of beef meat, a detergent and a shopping time, conventionally, it is almost impossible to find out the combination. Particularly, in the case of big data that has recently been a hot topic, generally, while a plurality of “tendencies” having various correlations are hidden therein, the amount of data is vast, and accordingly, it is further difficult to find out even a set having a correlation between two elements.
Meanwhile, when a set having multidimensional correlated elements and the correlated elements are determined, data to be analyzed and dimensions (=elements) of the analysis are largely refined, and accordingly, it is easy to make various and deep analyses therefrom. Thus, how to find a set having a multidimensional correlation and the correlated elements thereof in an easy manner may be regarded as the most urgent task for a big data analysis.